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GNU stow
is an underrated tool. Generically, it helps maintain a unified tree of files that come from different sources. More
concretely, I use a bunch of software (D compilers, various tools) that I install manually instead of through my
system’s package manager (for various reasons). stow makes
that maintainable by letting me cleanly add/remove packages and switch between versions. Here’s how it’s done.
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This started with a consulting snafu: Government organisation A got government organisation B to develop a web application. Government organisation B subcontracted part of the work to somebody. Hosting and maintenance of the project was later contracted out to a private-sector company C. Company C discovered that the subcontracted somebody (who was long gone) had built a custom Docker image and made it a dependency of the build system, but without committing the original Dockerfile. That left company C with a contractual obligation to manage a Docker image they had no source code for. Company C calls me in once in a while to do various things, so doing something about this mystery meat Docker image became my job.
Fortunately, the Docker image format is a lot more transparent than it could be. A little detective work is needed, but a lot can be figured out just by pulling apart an image file. As an example, here’s a quick walkthrough of an image for the Prettier code formatter. (In fact, it’s so easy, there’s a tool for it. Thanks Ezequiel Gonzalez Rial.)
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Some work (like programming) takes a lot of concentration, and I use noise-cancelling headphones to help me work productively in silence. But for other work (like doing business paperwork), I prefer to have quiet music in the background to help me stay focussed. Quiet background music is good for meditation or dozing, too. If you can’t fall asleep or completely clear your mind, zoning out to some music is the next best thing.
The best music for that is simple and repetitive — something nice enough to listen too, but not distracting, and okay to tune out of when needed. Computer game music is like that, by design, so there’s plenty of good background music out there. The harder problem is finding samples that play for more than a few minutes.
So I made loopx, a tool that takes a sample of music that loops a few times,
and repeats the loop to make a long piece of music.
When you’re listening to the same music loop for a long time, even slight distortion becomes distracting. Making quality extended music audio out of real-world samples (and doing it fast enough) takes a bit of maths and computer science. About ten years ago I was doing digital signal processing (DSP) programming for industrial metering equipment, so this side project got me digging up some old theory again.
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Code generators can be useful tools. I sometimes use the command line version of Jinja2 to generate highly redundant config files and other text files, but it’s feature-limited for transforming data. Obviously the author of Jinja2 thinks differently, but I wanted something like list comprehensions or D’s composable range algorithms.
I decided to make a tool that’s like Jinja2, but lets me generate complex files by transforming data with range
algorithms. The idea was dead simple: a templating language that gets rewritten directly to D code. That way it
supports everything D does, simply because it is D. I wanted a standalone code generator, but thanks to
D’s mixin
feature, the same templating language works as an embedded templating language (for HTML in a web app, for
example). (For more on that trick, see this post about translating
Brainfuck to D to machine code all at compile time using mixins.)
As usual, it’s on GitLab. The examples in this post can be found there, too.
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If you do any server administration work, you’ll have worked with log files. And if your servers need to be reliable, you’ll know that log files are common source of problems, especially when you need to rotate or ship them (which is practically always). In particular, moving files around causes race conditions.
Thankfully, there are better ways. With named pipes, you can have a simple and robust logging stack, with no race conditions, and without patching your servers to support some network logging protocol.
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Translations:русский
Most of my paid work involves deploying software systems, which means I spend a lot of time trying to answer the following questions:
That’s a kind of debugging, but it’s a different kind of debugging from normal software debugging. Normal debugging is usually about the logic of the code, but deployment debugging is usually about the interaction between the code and its environment. Even when the root cause is a logic bug, the fact that the software apparently worked on another machine means that the environment is usually involved somehow.
So, instead of using normal debugging tools like gdb, I
have another toolset for debugging deployments. My favourite tool for “Why isn’t this software working on this
machine?” is strace.
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KLEE is a symbolic execution engine that can rigorously verify or find bugs in
software. It’s designed for C and C++, but it’s just an interpreter for LLVM bitcode combined with theorem prover
backends, so it can work with bitcode generated by ldc2. One
catch is that it needs a compatible bitcode port of the D runtime to run normal D code. I’m still interested in getting
KLEE to work with normal D code, but for now I’ve done some experiments with -betterC D.
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Recently I’ve been playing around with using bpftrace to trace and profile D’s garbage collector. Here are some
examples of the cool stuff that’s possible.
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Back in 2001 Joel Spolsky wrote his classic essay “Good Software Takes Ten Years. Get Used To it”. Nothing much has changed since then: software is still taking around a decade of development to get good, and the industry is still getting used to that fact. Unfortunately, the industry has investors who want to see hockey stick growth rates on software that’s a year old or less. The result is an antipattern I like to call “Hello World Marketing”. Once you start to notice it, you see it everywhere, and it’s a huge red flag when choosing software tools.
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Here are the slide decks to a couple of talks I’ve given recently.
Because this talk is based on my own experiences, it’s particularly relevant to service businesses in Australia. But if you’re interested in being your own boss, anywhere or anyhow, you could find it useful. As I said in the talk, there’s a lot of stuff that feels obvious to me now, but I ended up learning the hard way.
Here’s a common story: Devs write an app, and do all the right things like using source control and writing automated test suites. Then it comes to deploy the code, and they have to figure out all these things like DNS and server infrastructure. They hack something together using web UIs, but six months later no one can remember the deployment process any more.
This presentation was a really quick introduction to the tools you can use to get more app dependencies into source control.
